How Accurate is a Cardiac Monitor Algorithm?

A new study published in Nature Medicine shows that a deep learning algorithm can learn to detect and classify arrhythmias with the accuracy of a cardiologist. See how Zio can give you the reliability of a human expert.

“It’s imperative… that you give thought to what type of algorithm and therefore information you will be receiving on that patient.” — Dr. Martin Maron, Director of the Hypertrophic Cardiomyopathy Center at Tufts Medical Center

How Could Artificial Intelligence (AI) Impact Cardiology?

AI technology can expedite comprehension of large ECG data sets, boosting clinical efficiency and letting physicians focus on patient care rather than having to re-look at questionable strips. However, not all algorithms are created equal. It’s important to be discerning about algorithm quality when choosing a service for your practice.

Understanding cardiac monitor algorithm performance

Every monitoring service processes data differently. The quality of input data and detection thresholds for various arrhythmias can vary. You may want to find out:

How many arrhythmias can be accurately detected?

What are the detection thresholds for each class of arrhythmia?

Has the algorithm been validated by peer-reviewed studies?

Why Algorithm Quality Should Matter to Your Practice

Accuracy in your cardiac monitoring algorithm is crucial for making care decisions. However, performance and output quality are dependent on the expansiveness and diversity of input data and can vary greatly between different solutions. Therefore, it’s especially important to understand if your solution is at risk of missing critical information.

We put Zio’s data to the test.

In collaboration with the Stanford Machine Learning Group, over 91,000 Zio records were processed through a deep neural network (DNN) to see if the algorithm could learn to identify various arrhythmia classes.

The quality and quantity of Zio data enabled the DNN to accurately detect and identify 10 arrhythmia classes while separating sinus rhythm and artifact for a total of 12 output classes. The results were compared to those of board-certified experts confirming equivalent performance.

Keep in mind that a deep learning algorithm requires a large amount of quality data to be accurately trained. This collaboration demonstrated the first deep learning model to accurately label 10 arrhythmia classes which was achieved using large amounts of diverse data generated by the Zio Service.